This series of files compile all analyses done during Chapter 3:

All analyses have been done with R 4.0.2.

Click on the table of contents in the left margin to assess a specific analysis.
Click on a figure to zoom it

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Sources of activity considered for the analyses:

Fisheries data considered for the analyses (expressed as number of fishing events or kilograms of collected individuals for each gear):

Gear Code Years Events Species
Dredge FishDred 2010-2014 21 Mactromeris polynyma
Net FishNet 2010 5 Clupea harengus, Gadus morhua
Trap FishTrap 2010-2015 1061 Buccinum sp., Cancer irroratus, Chionoecetes opilio, Homarus americanus
Bottom-trawl FishTraw 2013-2014 2 Pandalus borealis

1. Methodology

The aim of this section is to predict the structure of benthic communities based on the values of environmental variables.

We used abiotic parameters and indices of human exposure indices (calculated in Section 1) as predictors. We tested different methods: GLMs, GAMs, Random Forest and HMSC. Each method has been developed in dedicated scripts, whose final objects were imported here to present results and trends.

For each method, results are presented with a table regrouping McFadden’s or Tjur’s pseudo-R2, validation ratios and variables coefficients, and with maps displaying the probability of presence of each taxon for which the pseudo-R2 is higher than 0.20 (and different than 1). The raster presents results of the SDM (grey: low probability, dark blue: high). Stations are either plotted with colors (green = taxa present, red = taxon absent) or with circles (wider circcles = higher taxon density).

2. Models

2.1. Generalized Linear Models

Diagnostics for each model can be found here.

2.1.1. Presence/absence data

We considered presence/absence data with a binomial distribution.

Abiotic parameters

Presence probability of significative taxa:

Prediction of specific richness based on this model:

Exposure indices

Presence probability of significative taxa:

Prediction of specific richness based on this model:

2.1.2. Density data

⚠️ To be added … or not

2.2. Hierarchical Models of Species Communities

This section uses methodology and tools from Ovaskainen et al., with the direct help of Guillaume Blanchet.

First, we will compute models using the 108 stations with abiotic variables or exposure indices as predictors. 85 % of the stations (92) will act as training data, and the rest (16) will be used to validate the outputs. Second, these models will be used to predict taxa richness and distribution in the entire study area using predictor rasters.

We initiate the HMSC model with the chosen data:

  • presence/absence or density for dependant variable
  • exposure indices or abiotic variables for predictors

Priors and model parameters are set in the hmsc() function.

HMSC_PA <- hmsc(data, param, prior, family = "probit", niter = 100000, nburn = 1000, thin = 100)
HMSC_density <- hmsc(data, param, prior, family = "overPoisson", niter = 100000, nburn = 1000, thin = 100)

Here are the outcomes and diagnostics to evaluate each model’s quality (presented for each species seperately or averaged).

Diagnostics for each model can be found here.

2.2.1. Presence/absence data

We considered presence/absence data with a probit distribution.

Abiotic parameters

Mean of the predictor coefficients estimated by the HMSC model:

95 % confidence interval of the predictor coefficients estimated by the HMSC model:

Predictive power of the HMSC model:

Variance partitioning:

Presence probability of significative taxa:

Prediction of specific richness:

Exposure indices

Mean of the predictor coefficients estimated by the HMSC model:

95 % confidence interval of the predictor coefficients estimated by the HMSC model:

Predictive power of the HMSC model:

Variance partitioning:

Presence probability of significative taxa:

Prediction of specific richness:

2.2.2. Density data

⚠️ To be added … or not

2.3. Generalized Additive Models

2.3.1. Presence/absence data

⚠️ To be added … or not

2.3.2. Density data

⚠️ To be added … or not

2.4. Random Forest Algorithms

2.4.1. Presence/absence data

⚠️ To be added … or not

2.4.2. Density data

⚠️ To be added … or not

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